4. Infinite Newscasters

I want to create a task which lets me generate an mp4 video of an AI avatar talking about a topic for 1 minute. The initial user input would be 1) a prompt that describes the newscaster (eg woman in news studio with red hat), and 2) the topic of the video and what the newscaster should talk about. We can use Leonardo AI to make the image of the avatar, an LLM like GPT4 to make the transcription, Elevenlabs to make the voice-over, and D-ID to convert the mp3 voice-over with avatar image URL to a final video. Then, as the voice-over is talking we will display stock footage on top of the video. To do this we will need to search Pexels etc for royalty-free images, and then use Shotstack to overlay the videos on top of the video timeline. We shouldn't overdo this, so perhaps put 5 videos on top of the timeline for 5 seconds apart (so approx half the length of the video). All of these API calls are within my agent.

Sub Agent #1 | Sub Agent #2 | Sub Agent #3

CLAUDE:- final-output-and-component-outputs

Thank you for the detailed description of your project. Let me address your questions one by one: a) The final output of this automated workflow would be an mp4 video file. This video would contain an AI-generated avatar of a newscaster speaking about a given topic for 1 minute. The avatar's appearance would be based on the user's prompt (e.g., a woman in a news studio with a red hat). The video would also have relevant stock footage overlaid on top of the avatar at specific intervals (approximately 5 videos, each appearing for 5 seconds, spread evenly throughout the 1-minute duration). b) To create this final output, the task can be broken down into the following core components: 1. Avatar Image: An image of the AI-generated newscaster avatar, created using Leonardo AI based on the user's prompt. The output would be a high-resolution image file (e.g., PNG or JPEG). 2. Video Script: A text file containing the transcription of what the newscaster will say about the given topic. This script would be generated by an LLM like GPT-4 based on the user's input about the video's subject matter. The output would be a plain text file or a formatted document (e.g., TXT, DOCX, or PDF). 3. Voice-over Audio: An audio file of the AI-generated voice speaking the video script. This would be created using Elevenlabs' text-to-speech API, which would convert the video script into a realistic human voice. The output would be an audio file (e.g., MP3 or WAV). 4. Talking Head Video: A video file of the AI avatar speaking the voice-over audio. This would be generated using the D-ID API, which would combine the avatar image and the voice-over audio to create a lip-synced video of the newscaster speaking. The output would be a video file (e.g., MP4 or MOV). 5. Stock Footage: A collection of 5 royalty-free video clips relevant to the video's topic. These clips would be sourced from platforms like Pexels using their API. The output would be 5 separate video files (e.g., MP4 or MOV). 6. Shotstack JSON Configuration: A JSON file containing the timeline and layout instructions for combining the talking head video with the stock footage. This file would specify the timing for each stock footage clip (5 seconds each, evenly distributed) and any other necessary parameters. The output would be a JSON file. 7. Final Composite Video: The final mp4 video file, which would be generated by the Shotstack API based on the JSON configuration. This video would combine the talking head video with the overlaid stock footage according to the specified timeline. These components would need to be generated in a specific order, with the output of one step serving as the input for the next. The AI agent would need to manage the flow of data between the various APIs and ensure that the final composite video meets the user's requirements.

GPT4:- final-output-and-component-outputs

a) The final output of this process will be an mp4 video. Specifically, it would be a one-minute video of an AI avatar (produced from a user's description, for instance, a woman in a news studio with a red hat) delivering a news piece on a user-defined subject. The video would contain the AI avatar talking, supplemented by relevant stock footage displayed at distinct intervals throughout the timeline. b) Breaking up the task into its component parts, the core components required to generate the final output are: - An AI-generated avatar, using Leonardo AI, based on a user's description. The output here is the image/URL of the avatar. - A transcription to act as the news speech, generated by an LLM like GPT4 based on a user given topic. This output is a formatted text script that the AI avatar will be speaking. - A voice-over for the AI avatar speaking, developed using Elevenlabs using the previously generated transcription. The output is an mp3 file or URL. - A final video combining the AI avatar and the voice-over, made using D-ID. The output at this stage will be an mp4 video where the AI avatar appears to be speaking the news script. - Related stock footage to overlay on the video timeline. This will require searching Pexels based on relevant keywords, and then obtaining multiple royalty-free videos/images. The output will be URLs of selected stock footage. - A video timeline/JSON file that serves as a programmed storyboard detailing when and where to overlay the stock footage onto the avatar video. This timeline will be used by Shotstack to integrate the stock footage into the final video mp4. It will specify when each stock footage starts, ends, which portion of the main video it covers and any other parameters (e.g., opacity). The above list captures the outputs required along each step of the production process. All of these need to come together to produce the final output - an engaging one-minute mp4 video of an AI avatar delivering a piece of news.

claude 3 - extract final output

The final output of this automated workflow would be an mp4 video file. This video would contain an AI-generated avatar of a newscaster speaking about a given topic for 1 minute. The avatar's appearance would be based on the user's prompt (e.g., a woman in a news studio with a red hat). The video would also have relevant stock footage overlaid on top of the avatar at specific intervals (approximately 5 videos, each appearing for 5 seconds, spread evenly throughout the 1-minute duration).

extract component outputs

To create this final output, the task can be broken down into the following core components: 1. Avatar Image: An image of the AI-generated newscaster avatar, created using Leonardo AI based on the user's prompt. The output would be a high-resolution image file (e.g., PNG or JPEG). 2. Video Script: A text file containing the transcription of what the newscaster will say about the given topic. This script would be generated by an LLM like GPT-4 based on the user's input about the video's subject matter. The output would be a plain text file or a formatted document (e.g., TXT, DOCX, or PDF). 3. Voice-over Audio: An audio file of the AI-generated voice speaking the video script. This would be created using Elevenlabs' text-to-speech API, which would convert the video script into a realistic human voice. The output would be an audio file (e.g., MP3 or WAV). 4. Talking Head Video: A video file of the AI avatar speaking the voice-over audio. This would be generated using the D-ID API, which would combine the avatar image and the voice-over audio to create a lip-synced video of the newscaster speaking. The output would be a video file (e.g., MP4 or MOV). 5. Stock Footage: A collection of 5 royalty-free video clips relevant to the video's topic. These clips would be sourced from platforms like Pexels using their API. The output would be 5 separate video files (e.g., MP4 or MOV). 6. Shotstack JSON Configuration: A JSON file containing the timeline and layout instructions for combining the talking head video with the stock footage. This file would specify the timing for each stock footage clip (5 seconds each, evenly distributed) and any other necessary parameters. The output would be a JSON file. 7. Final Composite Video: The final mp4 video file, which would be generated by the Shotstack API based on the JSON configuration. This video would combine the talking head video with the overlaid stock footage according to the specified timeline. These components would need to be generated in a specific order, with the output of one step serving as the input for the next. The AI agent would need to manage the flow of data between the various APIs and ensure that the final composite video meets the user's requirements.

Claude - research-ideas-and-input-variables

Thank you for the detailed description of the video creation task. Let me address your questions one by one: a) Clarifying questions for the final output: 1. What should be the exact duration of the video? (e.g., 60 seconds, 90 seconds) 2. Is there a specific gender, age, or ethnicity preference for the AI avatar? 3. Should the AI avatar maintain a serious news anchor tone or adopt a more casual, conversational style? 4. Are there any specific requirements for the stock footage (e.g., related to the topic, neutral b-roll footage)? 5. What format and resolution should the final video be in? (e.g., MP4, 1080p) b) Required inputs before launching the task: 1. A detailed prompt describing the AI avatar's appearance and setting. 2. The topic or script of the video, outlining what the AI avatar should talk about. 3. Any specific keywords or themes to consider when searching for stock footage. 4. The target audience for the video (if applicable). c) Potential task segmentation: 1. Avatar creation: This could be a separate task focusing on generating the AI avatar using Leonardo AI, ensuring that it meets the specified requirements and aligns with the video's intended tone and style. 2. Script generation: The process of creating the video script could be a distinct task, allowing for iterations and refinements to ensure the content is engaging, informative, and aligns with the video's objectives. 3. Stock footage selection: Searching for and curating appropriate stock footage could be a separate task, ensuring that the selected clips enhance the video's message without being overly distracting. 4. Audio generation: Creating the voice-over using Elevenlabs could be a standalone task, focusing on achieving the desired tone, pacing, and clarity in the AI avatar's speech. 5. Video editing and composition: The final task would involve bringing all the elements together using D-ID and Shotstack, ensuring smooth transitions between the AI avatar and stock footage, and creating a cohesive final product. By segmenting these tasks, your AI Agent can focus on each aspect individually, allowing for better quality control and refinements at each stage before moving on to the next task in the workflow.

GPT - research-ideas-and-input-variables

a) To better understand the final output of the task, here are some questions that I might ask: 1. What specific topic should the AI newscaster talk about? Is it a general topic or something specific? 2. How detailed should the transcription generated by the LLM be? Is it a general outline or a crisp 1-minute speech? 3. What is the specific description of the avatar? Are there any detailed physical characteristics or actions that need to be included (like glasses, hair color, clothes, etc.)? 4. While using Shotstack to overlay videos, what kind of stock footage should be used? Should they match the topic being talked about or should they be generic images and videos of a news studio? 5. Would there be specific editing instructions for the video? Like pauses, zooms, transitions, etc.? b) Here's what I believe the task should accept as input before launching the task completion process: 1. Specific clear description of the AI newscaster avatar: Consisting of gender, apparel, location (in this case, a news studio), additional appearance details like a red hat, glasses, etc. 2. Detailed topic for the news item: This should be specific and clear enough to generate a meaningful 1-minute transcription. 3. Any specific stylistic preferences for the generated transcription: This could include the tone, language level and complexity, target audience, etc. 4. Instructions for stock footages overlay: Indications on what type of stock footages are preferred for overlay on video timeline and any specific timings or cues for when to transition between these. 5. Additional specific instructions for video editing, if any. These inputs will guide the AI Agent in the process to ensure the final output matches the desired outcome.

template-ideas

Based on the description of the final output for your automated workflow, here are some templates and resources that could help refine the definition of a successful output: 1. News broadcast videos: Search for and analyze professional news broadcast videos on YouTube or news websites. Pay attention to the following aspects: - The appearance and attire of the newscaster - The background or studio setup - The duration of the video and the pacing of the content - The use of stock footage or graphics to supplement the main content 2. AI-generated avatar videos: Look for existing examples of videos that feature AI-generated avatars, such as those created using the D-ID platform or similar tools. Analyze the following: - The quality and realism of the avatar - The lip-syncing and facial expressions of the avatar - The overall visual aesthetics of the video 3. News article summaries: Since the AI newscaster will be speaking about a given topic, search for news articles on various topics and read their summaries or abstracts. This will provide insight into: - The ideal length and structure of a 1-minute news summary - The type of information that should be included in the summary - The tone and style of the content 4. Stock footage websites: Visit popular stock footage websites like Pexels, Shutterstock, or Getty Images. Search for footage related to various news topics and analyze: - The quality and resolution of the footage - The relevance of the footage to the news topic - The duration of the clips and how they could be used to enhance the main content 5. Video editing templates: Look for video editing templates or presets in software like Adobe Premiere Pro, Final Cut Pro, or online video editing platforms. These templates can provide ideas for: - The overall structure and pacing of a 1-minute video - The placement and duration of stock footage clips - The use of transitions, graphics, or other visual elements By collecting and analyzing these templates and resources, you can gain valuable insights into what constitutes a successful output for your automated workflow. This will help you refine your definition of the ideal end product and guide the development of your AI-powered video creation process.

claude 3 - searches

Here are 5 specific Google search keyphrases to help refine the definition of a successful output for your AI-generated newscaster video: 1. "1 minute news summary example video" - This search will help find examples of short news summary videos that showcase the ideal structure, pacing, and content for a 1-minute news segment. 2. "AI generated newscaster video D-ID" - Searching for AI-generated newscaster videos specifically created using the D-ID platform will provide insights into the quality, realism, and visual aesthetics of the avatar, as well as the lip-syncing and facial expressions. 3. "female newscaster in red hat studio background shutterstock" - This keyphrase will help locate stock images or footage of female newscasters in a studio setting wearing a red hat, which can serve as a visual reference for the appearance and attire of the AI-generated avatar. 4. "news topic stock footage pexels" - Searching for stock footage related to various news topics on the Pexels website will give you an idea of the quality, relevance, and duration of clips that can be used to enhance the main content of your AI-generated video. 5. "1 minute news video template adobe premiere pro" - Looking for 1-minute news video templates specifically designed for Adobe Premiere Pro will provide insights into the overall structure, pacing, placement, and duration of stock footage clips, as well as the use of transitions, graphics, or other visual elements in a professional news video. By searching for these specific keyphrases and reviewing the top results, you should be able to gather valuable templates and examples that will help refine your understanding of what constitutes a successful output for your AI-generated newscaster video project.

subagent-summary

A) FULL TASK SUMMARY: Create a 1-minute video of an AI-generated newscaster avatar discussing a user-specified topic, with relevant stock footage overlaid throughout the video. B) FINAL TASK OUTPUT: A 1-minute MP4 video file, 1920x1080 resolution, H.264 codec, 30 frames per second, with an AI-generated newscaster avatar lip-synced to a voice-over discussing the user-specified topic, and 5 stock footage clips (each 5 seconds long) overlaid evenly throughout the video. C) COMPONENTS OVERVIEW: 1. Avatar Image (PNG or JPEG) 2. Video Script (TXT, DOCX, or PDF) 3. Voice-over Audio (MP3 or WAV) 4. Talking Head Video (MP4 or MOV) 5. Stock Footage (5 MP4 or MOV files) 6. Shotstack JSON Configuration (JSON) 7. Final Composite Video (MP4) D) OVERVIEW OF SUBAGENTS: 1. Avatar Generator: Creates the newscaster avatar image based on user prompt. 2. Script Writer: Generates the video script based on the user-specified topic. 3. Voice-over Producer: Converts the video script into a realistic voice-over audio file. 4. Talking Head Video Creator: Combines the avatar image and voice-over audio to create a lip-synced video. 5. Stock Footage Curator: Searches and selects relevant stock footage clips based on the video topic. 6. Video Compositor: Generates the Shotstack JSON configuration and creates the final composite video. E) SUBAGENT 1: Avatar Generator - Name: Leonardo AI Avatar Creator - Final Output: High-resolution PNG or JPEG image of the newscaster avatar - Process: 1. Receive user prompt describing the newscaster's appearance 2. Use Leonardo AI's image generation API to create the avatar image based on the prompt 3. Refine the image using Leonardo AI's image editing capabilities, if necessary 4. Save the final avatar image in PNG or JPEG format - Sequence: User Prompt > Image Generation > Image Refinement (if needed) > Avatar Image Output F) SUBAGENT 2: Script Writer - Name: GPT-4 Video Script Generator - Final Output: Plain text file (TXT), Word document (DOCX), or PDF containing the video script - Process: 1. Receive user input about the video's topic and what the newscaster should discuss 2. Use GPT-4 to generate a 1-minute video script based on the user's input 3. Format the script with appropriate paragraphs, sentences, and punctuation 4. Save the final script in TXT, DOCX, or PDF format - Sequence: User Topic Input > Script Generation > Script Formatting > Video Script Output G) SUBAGENT 3: Voice-over Producer - Name: Elevenlabs Voice-over Creator - Final Output: MP3 or WAV audio file containing the voice-over - Process: 1. Receive the video script file from the Script Writer subagent 2. Use Elevenlabs' text-to-speech API to convert the script into a realistic human voice 3. Adjust the voice's speed, tone, and emotions to match the video's intended style 4. Save the final voice-over in MP3 or WAV format - Sequence: Video Script Input > Text-to-Speech Conversion > Voice Adjustment > Voice-over Audio Output H) SUBAGENT 4: Talking Head Video Creator - Name: D-ID Lip-Sync Video Generator - Final Output: MP4 or MOV video file of the newscaster avatar speaking the voice-over - Process: 1. Receive the avatar image from the Avatar Generator subagent 2. Receive the voice-over audio from the Voice-over Producer subagent 3. Use D-ID's lip-sync API to combine the avatar image and voice-over audio 4. Generate a video of the avatar speaking the voice-over with synchronized lip movements 5. Save the final talking head video in MP4 or MOV format - Sequence: Avatar Image Input + Voice-over Audio Input > Lip-Sync Video Generation > Talking Head Video Output I) SUBAGENT 5: Stock Footage Curator - Name: Pexels Stock Video Selector - Final Output: 5 MP4 or MOV video files containing relevant stock footage - Process: 1. Receive the user-specified video topic from the Script Writer subagent 2. Use Pexels' API to search for royalty-free video clips related to the topic 3. Select 5 video clips that are visually appealing and relevant to the script 4. Download the selected video clips in MP4 or MOV format 5. Save the stock footage files for use in the final composite video - Sequence: Video Topic Input > Stock Footage Search > Video Selection > Stock Footage Output J) SUBAGENT 6: Video Compositor - Name: Shotstack Video Editor - Final Output: JSON configuration file and final MP4 composite video - Process: 1. Receive the talking head video from the Talking Head Video Creator subagent 2. Receive the 5 stock footage clips from the Stock Footage Curator subagent 3. Create a JSON configuration file specifying the timeline and layout for combining the talking head video with the stock footage 4. Use Shotstack's API to generate the final composite video based on the JSON configuration 5. Ensure the stock footage clips are evenly distributed throughout the 1-minute video, each appearing for 5 seconds 6. Save the final composite video in MP4 format - Sequence: Talking Head Video Input + Stock Footage Input > JSON Configuration Creation > Composite Video Generation > Final Video Output

subagent-summary-amended-for-actual-tasks

Understood. Based on the updated list of available tasks, I have revised the subagents to complete the task of creating a 1-minute video of an AI-generated newscaster avatar discussing a user-specified topic, with relevant stock footage overlaid throughout the video. A) FULL TASK SUMMARY: Create a 1-minute video of an AI-generated newscaster avatar discussing a user-specified topic, with relevant stock footage overlaid throughout the video. B) FINAL TASK OUTPUT: A 1-minute MP4 video file, 1920x1080 resolution, H.264 codec, 30 frames per second, with an AI-generated newscaster avatar lip-synced to a voice-over discussing the user-specified topic, and 5 stock footage clips (each 5 seconds long) overlaid evenly throughout the video. C) COMPONENTS OVERVIEW: 1. Avatar Image (JPEG) 2. Video Script (TXT) 3. Voice-over Audio (MP3) 4. Talking Head Video (MP4) 5. Stock Footage (5 MP4 files) 6. Shotstack JSON Configuration (JSON) 7. Final Composite Video (MP4) D) OVERVIEW OF SUBAGENTS: 1. Avatar Generator: Creates the newscaster avatar image based on user prompt. 2. Script Writer: Generates the video script based on the user-specified topic. 3. Voice-over Producer: Converts the video script into a realistic voice-over audio file. 4. Talking Head Video Creator: Combines the avatar image and voice-over audio to create a lip-synced video. 5. Stock Footage Curator: Searches and selects relevant stock footage clips based on the video topic. 6. Video Compositor: Generates the Shotstack JSON configuration and creates the final composite video. E) SUBAGENT 1: Avatar Generator - Final Output: High-resolution JPEG image of the newscaster avatar - Process: 1. Use task #24 (Create photo image with LeonardoAI) to generate a realistic photo-style image of the newscaster avatar based on the user's prompt. - Sequence: User Prompt > Task #24 > Avatar Image Output F) SUBAGENT 2: Script Writer - Final Output: Plain text file (TXT) containing the video script - Process: 1. Use task #48 (Create article) to generate a 1-minute video script based on the user's input about the video's topic and what the newscaster should discuss. - Sequence: User Topic Input > Task #48 > Video Script Output G) SUBAGENT 3: Voice-over Producer - Final Output: MP3 audio file containing the voice-over - Process: 1. Use task #31 (Create voice-over from text) to convert the video script into a realistic human voice-over. - Sequence: Video Script Input > Task #31 > Voice-over Audio Output H) SUBAGENT 4: Talking Head Video Creator - Final Output: MP4 video file of the newscaster avatar speaking the voice-over - Process: 1. Use a lip-sync video generation API (REQUIRED) to combine the avatar image and voice-over audio, generating a video of the avatar speaking the voice-over with synchronized lip movements. - Sequence: Avatar Image Input + Voice-over Audio Input > Lip-Sync Video Generation API > Talking Head Video Output I) SUBAGENT 5: Stock Footage Curator - Final Output: 5 MP4 video files containing relevant stock footage - Process: 1. Use task #19 (Search Google images) to find relevant stock footage based on the video topic. 2. Use task #43 (Download YouTube video to mp4) to download the selected stock footage clips as MP4 files. - Sequence: Video Topic Input > Task #19 > Task #43 > Stock Footage Output J) SUBAGENT 6: Video Compositor - Final Output: JSON configuration file and final MP4 composite video - Process: 1. Use task #42 (Shotstack create video with text + images) to generate the final composite video by combining the talking head video with the stock footage clips according to the specified timeline. - Sequence: Talking Head Video Input + Stock Footage Input > Task #42 > Final Video Output

random-autocomplete

To achieve the end goal of creating a 1-minute mp4 video with an AI-generated newscaster avatar discussing a given topic, with relevant stock footage overlaid at specific intervals, the most relevant tasks might be: 1. Generating the AI avatar image based on the user's prompt using Leonardo AI. 2. Creating a transcript of the newscaster's speech about the given topic using an LLM like GPT-4. 3. Generating a voice-over for the transcript using Elevenlabs. 4. Searching for relevant royalty-free stock footage on platforms like Pexels. 5. Combining the AI avatar image and voice-over to create a talking head video using D-ID. 6. Overlaying the selected stock footage on the talking head video at specific intervals using Shotstack. 7. Exporting the final video as an mp4 file. The overall sequence of actions would be: 1. User Input > 2. Generate AI Avatar (Leonardo AI) > 3. Generate Transcript (GPT-4) > 4. Generate Voice-Over (Elevenlabs) > 5. Search Stock Footage (Pexels) > 6. Combine Avatar and Voice-Over (D-ID) > 7. Overlay Stock Footage (Shotstack) > 8. Export Final MP4 Video The Agent can accomplish this task by following these steps: 1. Accept user input for the newscaster's appearance and the topic of the video. 2. Use the Leonardo AI API to generate an image of the newscaster avatar based on the user's prompt. 3. Feed the video topic into GPT-4 to generate a 1-minute transcript of the newscaster's speech. 4. Use the Elevenlabs API to create a voice-over of the generated transcript. 5. Search for relevant royalty-free stock footage on Pexels using the video topic as keywords. 6. Combine the AI avatar image and voice-over using the D-ID API to create a talking head video. 7. Use the Shotstack API to overlay the selected stock footage on the talking head video at 5-second intervals. 8. Export the final video as an mp4 file and deliver it to the user. By leveraging the various APIs and the Agent's capabilities, this complex task can be automated and completed efficiently, resulting in a professional-looking video with minimal human intervention.

claude 3 - mermaid-for-all

<!DOCTYPE html> <html> <head> <title>Mermaid Diagram Example</title> <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script> </head> <body> <div class="mermaid"> graph TD A[User Input: Avatar Description and Video Topic] --> B{Avatar Generator} B --> C{Script Writer} C --> D{Voice-over Producer} B --> E{Talking Head Video Creator} D --> E C --> F{Stock Footage Curator} E --> G{Video Compositor} F --> G G --> H[Final Output: 1-minute MP4 Video] </div> <script> mermaid.initialize({ startOnLoad: true }); </script> </body> </html>

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SubAgent #1

claude 3 - subagent1

F) SUBAGENT 2: Script Writer - Final Output: Plain text file (TXT) containing the video script - Process: 1. Use task #48 (Create article) to generate a 1-minute video script based on the user's input about the video's topic and what the newscaster should discuss. - Sequence: User Topic Input > Task #48 > Video Script Output

subagent1-refined

A) SUBAGENT SUMMARY: The Script Writer subagent generates a 1-minute video script based on the user's input about the video's topic and what the newscaster should discuss. B) FINAL TASK OUTPUT: A plain text file (TXT) containing the video script, which should be approximately 150-200 words in length (suitable for a 1-minute video), saved on the server with a URL like server/_public/timestamp/storage/video_script.txt. C) SUBAGENT INPUT: - User's text input (1-500 characters) describing the video topic and what the newscaster should discuss. D) SUBAGENT TASK SUMMARY: 1. User Input > Task #1 (GPT-4) - Process user input and generate a list of 3-5 key points the script should cover, each point being 1-2 sentences long. 2. Output of Step 1 > Task #48 (Create article) - Generate a 150-200 word script based on the key points from Step 1, written in a style suitable for a newscaster to read aloud. 3. Output of Step 2 > Task #9 (Save content to server) - Save the generated script as a plain text file (TXT) on the server with a URL like server/_public/timestamp/storage/video_script.txt. E) SILOS: This subagent does not require silos as the tasks can be completed sequentially without parallel processing. F) DETAILED TASK BREAKDOWN: 1. User Input Processing: - Input: User's text input (1-500 characters) describing the video topic and what the newscaster should discuss. - Task: #1 (GPT-4) - Prompt: "Please generate a list of 3-5 key points that a 1-minute newscaster script should cover based on the following user input: [USER_INPUT]. Each point should be 1-2 sentences long and capture the most important aspects of the topic." - Output: A list of 3-5 key points, each 1-2 sentences long, to be used as the basis for the video script. 2. Script Generation: - Input: The list of key points generated in Step 1. - Task: #48 (Create article) - Prompt: "Please write a 150-200 word script for a 1-minute newscaster video based on the following key points: [KEY_POINTS_LIST]. The script should be written in a style suitable for a newscaster to read aloud and should cover each point in a concise and engaging manner." - Output: A 150-200 word script for the 1-minute newscaster video. 3. Script Saving: - Input: The generated video script from Step 2. - Task: #9 (Save content to server) - Details: Save the video script as a plain text file (TXT) on the server with a URL like server/_public/timestamp/storage/video_script.txt. - Output: The URL of the saved video script file (e.g., server/_public/timestamp/storage/video_script.txt).

subagent1refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS): 1. User Input: - Task: Receive user input (1-500 characters) describing the video topic and what the newscaster should discuss. 2. Key Points Generation: - Task: #1 (GPT-4) - Prompt: "Please generate a list of 3-5 key points that a 1-minute newscaster script should cover based on the following user input: [USER_INPUT]. Each point should be 1-2 sentences long and capture the most important aspects of the topic." - Output: A list of 3-5 key points, each 1-2 sentences long, to be used as the basis for the video script. 3. Script Generation: - Input: The list of key points generated in Step 2. - Task: #48 (Create article) - Prompt: "Please write a 150-200 word script for a 1-minute newscaster video based on the following key points: [KEY_POINTS_LIST]. The script should be written in a style suitable for a newscaster to read aloud and should cover each point in a concise and engaging manner." - Output: A 150-200 word script for the 1-minute newscaster video. 4. Script Saving: - Input: The generated video script from Step 3. - Task: #9 (Save content to server) - Details: Save the video script as a plain text file (TXT) on the server with a URL like server/_public/timestamp/storage/video_script.txt. - Output: The URL of the saved video script file (e.g., server/_public/timestamp/storage/video_script.txt). In this specific subagent, there are no explicitly stated repetitions of tasks. The process involves a single user input, generating key points once, creating one script based on those key points, and saving that script to the server. Unless the user input indicates a need for multiple scripts or variations, the tasks do not need to be duplicated or replicated.

Subagent1 task-ID-numbers

1,48,9

subagent1-output-variables

G) FULL TASKS (INCLUDING REPEATING TASKS): 1. User Input: - Task: Receive user input (1-500 characters) describing the video topic and what the newscaster should discuss. - Output: [user-input] 2. Key Points Generation: - Task: #1 (GPT-4) - Prompt: "Please generate a list of 3-5 key points that a 1-minute newscaster script should cover based on the following user input: [user-input]. Each point should be 1-2 sentences long and capture the most important aspects of the topic." - Output: [key-points-list] 3. Script Generation: - Input: [key-points-list] - Task: #48 (Create article) - Prompt: "Please write a 150-200 word script for a 1-minute newscaster video based on the following key points: [key-points-list]. The script should be written in a style suitable for a newscaster to read aloud and should cover each point in a concise and engaging manner." - Output: [video-script] 4. Script Saving: - Input: [video-script] - Task: #9 (Save content to server) - Details: Save the video script as a plain text file (TXT) on the server with a URL like server/_public/timestamp/storage/video_script.txt. - Output: [video-script-url] H) OUTPUT TOKENS: - [user-input]: The user's input describing the video topic and what the newscaster should discuss (1-500 characters). - [key-points-list]: A list of 3-5 key points, each 1-2 sentences long, generated based on the user input to serve as the basis for the video script. - [video-script]: A 150-200 word script for the 1-minute newscaster video, written based on the key points and in a style suitable for a newscaster to read aloud. - [video-script-url]: The URL where the generated video script is saved as a plain text file (TXT) on the server, e.g., server/_public/timestamp/storage/video_script.txt. In this specific subagent, there are no explicitly stated repetitions of tasks. The process involves a single user input, generating key points once, creating one script based on those key points, and saving that script to the server. Unless the user input indicates a need for multiple scripts or variations, the tasks do not need to be duplicated or replicated, and thus, the output tokens do not require numbering or additional semantic identifiers.

subagent1refined-repeating-json-core

A) SUBAGENT SUMMARY: This subagent generates a video script based on user input, covering key points and saving the script to a server. B) FINAL TASK OUTPUT: A 150-200 word plain text file (TXT) containing a video script saved on the server with a URL like server/_public/timestamp/storage/video_script.txt. C) SUBAGENT INPUT: - User input (1-500 characters) describing the video topic and what the newscaster should discuss. E) SUBAGENT TASK SUMMARY: 1. Receive user input describing the video topic and content. 2. Generate 3-5 key points based on the user input using GPT-4. 3. Create a 150-200 word video script based on the key points using an article creation task. 4. Save the generated video script as a plain text file (TXT) on the server. F) SILOS: This subagent does not have any silos. G) TASKFLOW SUMMARY: Task 1: Title task: Receive User Input Description of the task: Receive user input (1-500 characters) describing the video topic and what the newscaster should discuss. Input(s) involved: [USER_INPUT] Output token name: [USER_INPUT] Task 2: Title task: Generate Key Points Description of the task: Generate a list of 3-5 key points, each 1-2 sentences long, based on the user input using GPT-4. Input(s) involved: [USER_INPUT] Output token name: [KEY_POINTS_LIST] Task 3: Title task: Create Video Script Description of the task: Write a 150-200 word script for a 1-minute newscaster video based on the generated key points using an article creation task. Input(s) involved: [KEY_POINTS_LIST] Output token name: [VIDEO_SCRIPT] Task 4: Title task: Save Video Script Description of the task: Save the generated video script as a plain text file (TXT) on the server with a URL like server/_public/timestamp/storage/video_script.txt. Input(s) involved: [VIDEO_SCRIPT] Output token name: [VIDEO_SCRIPT_URL]

subagent1mermaid

<!DOCTYPE html> <html> <head> <title>Mermaid Diagram Example</title> <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script> </head> <body> <div class="mermaid"> graph TD A[User Input] -->|USER_INPUT| B(Generate Key Points) B --> |KEY_POINTS_LIST| C(Create Video Script) C --> |VIDEO_SCRIPT| D(Save Video Script) D --> |VIDEO_SCRIPT_URL| E{Output} </div> <script> mermaid.initialize({ startOnLoad: true }); </script> </body> </html>

---

SubAgent #2

claude 3 - subagent2

[subagent2]

subagent2-refined

A) SUBAGENT SUMMARY: Script Writer generates a 1-minute video script based on the user-specified topic and what the newscaster should discuss. B) FINAL TASK OUTPUT: Plain text file (TXT) containing the video script, approximately 150-200 words in length, suitable for a 1-minute voice-over. C) SUBAGENT INPUT: - User-specified topic for the video (text, 1-100 characters) - Brief description of what the newscaster should discuss (text, 1-500 characters) E) SUBAGENT TASK SUMMARY: 1. Combine the user-specified topic and description of what the newscaster should discuss into a single text prompt. 2. Use task #2 (Prompt generator) to generate an optimized prompt for creating the video script based on the combined user input. 3. Use task #1 (GPT4) with the optimized prompt to generate a video script of approximately 150-200 words, suitable for a 1-minute voice-over. 4. Use task #9 (Save content to server) to save the generated video script as a plain text file (TXT) on the server. Subagent sequence: User Topic Input + User Description Input > Task #2 (Prompt generator) > Task #1 (GPT4) > Task #9 (Save content to server) > Video Script Output (TXT file URL) F) SILOS: This subagent does not require silos as it involves a linear sequence of tasks without parallel processing.

subagent2refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS): 1. Receive user-specified topic for the video (text, 1-100 characters) 2. Receive brief description of what the newscaster should discuss (text, 1-500 characters) 3. Combine the user-specified topic and description of what the newscaster should discuss into a single text prompt 4. Use task #2 (Prompt generator) to generate an optimized prompt for creating the video script based on the combined user input 5. Use task #1 (GPT4) with the optimized prompt to generate a video script of approximately 150-200 words, suitable for a 1-minute voice-over 6. Use task #9 (Save content to server) to save the generated video script as a plain text file (TXT) on the server 7. Output the URL of the saved plain text file (TXT) containing the video script In this case, there are no explicit instructions for repeating tasks within the subagent. The tasks are performed sequentially without any repetitions or parallel processing. Therefore, the full task list remains the same as the subagent task summary, with each task listed individually.

Subagent2 task-ID-numbers

2,1,9

subagent2-output-variables

G) FULL TASKS (INCLUDING REPEATING TASKS): 1. Receive user-specified topic for the video (text, 1-100 characters) [video-topic] 2. Receive brief description of what the newscaster should discuss (text, 1-500 characters) [video-description] 3. Combine the user-specified topic and description of what the newscaster should discuss into a single text prompt [combined-prompt] 4. Use task #2 (Prompt generator) to generate an optimized prompt for creating the video script based on the combined user input [optimized-prompt] 5. Use task #1 (GPT4) with the optimized prompt to generate a video script of approximately 150-200 words, suitable for a 1-minute voice-over [video-script] 6. Use task #9 (Save content to server) to save the generated video script as a plain text file (TXT) on the server [saved-script-url] 7. Output the URL of the saved plain text file (TXT) containing the video script [saved-script-url] H) OUTPUT TOKENS [video-topic]: The user-specified topic for the video (text, 1-100 characters) [video-description]: Brief description of what the newscaster should discuss (text, 1-500 characters) [combined-prompt]: The combination of the user-specified topic and description of what the newscaster should discuss into a single text prompt [optimized-prompt]: The optimized prompt for creating the video script based on the combined user input, generated using task #2 (Prompt generator) [video-script]: The video script of approximately 150-200 words, suitable for a 1-minute voice-over, generated using task #1 (GPT4) with the optimized prompt [saved-script-url]: The URL of the saved plain text file (TXT) containing the video script, generated using task #9 (Save content to server)

subagent2refined-repeating-json-core

A) SUBAGENT SUMMARY: Generate a video script based on user-specified topic and description for a 1-minute AI newscaster video. B) FINAL TASK OUTPUT: A URL pointing to a plain text file (TXT) containing a video script of approximately 150-200 words, suitable for a 1-minute voice-over. C) SUBAGENT INPUT: - User-specified topic for the video (text, 1-100 characters) - Brief description of what the newscaster should discuss (text, 1-500 characters) E) SUBAGENT TASK SUMMARY: 1. Receive [user_topic] and [user_description] 2. Combine [user_topic] and [user_description] into [combined_prompt] 3. Use task #2 (Prompt generator) with [combined_prompt] to generate [optimized_prompt] 4. Use task #1 (GPT4) with [optimized_prompt] to generate [video_script] 5. Use task #9 (Save content to server) to save [video_script] as [script_txt_file] 6. Output [script_txt_url] F) SILOS: None G) TASKFLOW SUMMARY: Task 1: Title task: Receive user input for video topic and description Description of the task: Receive the user-specified topic for the video (1-100 characters) and a brief description of what the newscaster should discuss (1-500 characters). Input(s) involved: [user_topic], [user_description] Output token name: None Task 2: Title task: Combine user input into a single prompt Description of the task: Combine the user-specified topic and description of what the newscaster should discuss into a single text prompt. Input(s) involved: [user_topic], [user_description] Output token name: [combined_prompt] Task 3: Title task: Generate optimized prompt for video script Description of the task: Use task #2 (Prompt generator) to generate an optimized prompt for creating the video script based on the combined user input. Input(s) involved: [combined_prompt] Output token name: [optimized_prompt] Task 4: Title task: Generate video script using GPT4 Description of the task: Use task #1 (GPT4) with the optimized prompt to generate a video script of approximately 150-200 words, suitable for a 1-minute voice-over. Input(s) involved: [optimized_prompt] Output token name: [video_script] Task 5: Title task: Save video script as plain text file Description of the task: Use task #9 (Save content to server) to save the generated video script as a plain text file (TXT) on the server. Input(s) involved: [video_script] Output token name: [script_txt_file] Task 6: Title task: Output URL of the saved video script file Description of the task: Output the URL of the saved plain text file (TXT) containing the video script. Input(s) involved: [script_txt_file] Output token name: [script_txt_url]

subagent2mermaid

<!DOCTYPE html> <html> <head> <title>Mermaid Diagram Example</title> <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script> </head> <body> <div class="mermaid"> graph TD A[Receive user input for video topic and description] -->|user_topic, user_description| B[Combine user input into a single prompt] B -->|combined_prompt| C[Generate optimized prompt for video script] C -->|optimized_prompt| D[Generate video script using GPT4] D -->|video_script| E[Save video script as plain text file] E -->|script_txt_file| F[Output URL of the saved video script file] </div> <script> mermaid.initialize({ startOnLoad: true }); </script> </body> </html>

---

SubAgent #3

claude 3 - subagent3

G) SUBAGENT 3: Voice-over Producer - Final Output: MP3 audio file containing the voice-over - Process: 1. Use task #31 (Create voice-over from text) to convert the video script into a realistic human voice-over. - Sequence: Video Script Input > Task #31 > Voice-over Audio Output

subagent1-refined

A) SUBAGENT SUMMARY: The Voice-over Producer subagent converts a video script into a realistic human voice-over audio file. B) FINAL TASK OUTPUT: An MP3 audio file containing the voice-over, with a maximum duration of 1 minute (60 seconds), mono channel, 16-bit depth, and a sampling rate of 44.1 kHz. C) SUBAGENT INPUT: - Plain text file (TXT) containing the video script, with a maximum of 5,000 characters. D) SUBAGENT TASK SUMMARY: 1. Receive the video script as a plain text file (TXT) from the Script Writer subagent. 2. Use task #1 (GPT4) to analyze the video script and determine the appropriate voice settings (e.g., gender, age, tone) for the voice-over based on the content and target audience. 3. Use task #31 (Create voice-over from text) to convert the video script into a realistic human voice-over using the settings determined in step 2. 4. Save the generated voice-over as an MP3 file using task #9 (Save content to server). 5. Output the URL of the saved MP3 file containing the voice-over. Subagent sequence: Video Script (TXT) > Task #1 (Analyze script for voice settings) > Task #31 (Generate voice-over) > Task #9 (Save MP3 to server) > Voice-over Audio URL E) SILOS: This subagent does not require silos as the tasks can be performed sequentially without the need for parallel processing or complex dependencies.

subagent1refined-repeating

Here's the updated subagent structure with the "G) FULL TASKS (INCLUDING REPEATING TASKS)" section: A) SUBAGENT SUMMARY: The Voice-over Producer subagent converts a video script into a realistic human voice-over audio file. B) FINAL TASK OUTPUT: An MP3 audio file containing the voice-over, with a maximum duration of 1 minute (60 seconds), mono channel, 16-bit depth, and a sampling rate of 44.1 kHz. C) SUBAGENT INPUT: - Plain text file (TXT) containing the video script, with a maximum of 5,000 characters. D) SUBAGENT TASK SUMMARY: 1. Receive the video script as a plain text file (TXT) from the Script Writer subagent. 2. Use task #1 (GPT4) to analyze the video script and determine the appropriate voice settings (e.g., gender, age, tone) for the voice-over based on the content and target audience. 3. Use task #31 (Create voice-over from text) to convert the video script into a realistic human voice-over using the settings determined in step 2. 4. Save the generated voice-over as an MP3 file using task #9 (Save content to server). 5. Output the URL of the saved MP3 file containing the voice-over. Subagent sequence: Video Script (TXT) > Task #1 (Analyze script for voice settings) > Task #31 (Generate voice-over) > Task #9 (Save MP3 to server) > Voice-over Audio URL E) SILOS: This subagent does not require silos as the tasks can be performed sequentially without the need for parallel processing or complex dependencies. G) FULL TASKS (INCLUDING REPEATING TASKS): 1. Receive the video script as a plain text file (TXT) from the Script Writer subagent. 2. Use task #1 (GPT4) to analyze the video script and determine the appropriate voice settings (e.g., gender, age, tone) for the voice-over based on the content and target audience. 3. Use task #31 (Create voice-over from text) to convert the video script into a realistic human voice-over using the settings determined in step 2. 4. Save the generated voice-over as an MP3 file using task #9 (Save content to server). 5. Output the URL of the saved MP3 file containing the voice-over. In this case, there are no explicitly stated repeating tasks, so the "G) FULL TASKS (INCLUDING REPEATING TASKS)" section is the same as the "D) SUBAGENT TASK SUMMARY" section.

Subagent1 task-ID-numbers3

1,31,9

subagent3-output-variables

Here's the updated subagent structure with the "H) OUTPUT TOKENS" section: A) SUBAGENT SUMMARY: The Voice-over Producer subagent converts a video script into a realistic human voice-over audio file. B) FINAL TASK OUTPUT: An MP3 audio file containing the voice-over, with a maximum duration of 1 minute (60 seconds), mono channel, 16-bit depth, and a sampling rate of 44.1 kHz. C) SUBAGENT INPUT: - Plain text file (TXT) containing the video script, with a maximum of 5,000 characters. D) SUBAGENT TASK SUMMARY: 1. Receive the video script as a plain text file (TXT) from the Script Writer subagent. 2. Use task #1 (GPT4) to analyze the video script and determine the appropriate voice settings (e.g., gender, age, tone) for the voice-over based on the content and target audience. 3. Use task #31 (Create voice-over from text) to convert the video script into a realistic human voice-over using the settings determined in step 2. 4. Save the generated voice-over as an MP3 file using task #9 (Save content to server). 5. Output the URL of the saved MP3 file containing the voice-over. Subagent sequence: Video Script (TXT) > Task #1 (Analyze script for voice settings) > Task #31 (Generate voice-over) > Task #9 (Save MP3 to server) > Voice-over Audio URL E) SILOS: This subagent does not require silos as the tasks can be performed sequentially without the need for parallel processing or complex dependencies. G) FULL TASKS (INCLUDING REPEATING TASKS): 1. Receive the video script as a plain text file (TXT) from the Script Writer subagent. 2. Use task #1 (GPT4) to analyze the video script and determine the appropriate voice settings (e.g., gender, age, tone) for the voice-over based on the content and target audience. 3. Use task #31 (Create voice-over from text) to convert the video script into a realistic human voice-over using the settings determined in step 2. 4. Save the generated voice-over as an MP3 file using task #9 (Save content to server). 5. Output the URL of the saved MP3 file containing the voice-over. H) OUTPUT TOKENS: 1. [video-script]: The plain text file (TXT) containing the video script received from the Script Writer subagent. 2. [voice-settings]: The appropriate voice settings (e.g., gender, age, tone) determined by task #1 (GPT4) based on the content and target audience. 3. [voice-over-audio]: The realistic human voice-over audio file generated by task #31 (Create voice-over from text) using the [voice-settings]. 4. [voice-over-url]: The URL of the MP3 file containing the voice-over, saved to the server using task #9 (Save content to server).

subagent3refined-repeating-json-core

G) TASKFLOW SUMMARY Title task: Receive video script from Script Writer subagent Description of the task: The Voice-over Producer subagent receives the video script as a plain text file (TXT) from the Script Writer subagent. The script should have a maximum of 5,000 characters. Input(s) involved: [video_script.txt] Output token name: [video_script.txt] Title task: Analyze video script for voice-over settings Description of the task: Use GPT-4 to analyze the video script and determine the appropriate voice settings (e.g., gender, age, tone) for the voice-over based on the content and target audience. Input(s) involved: [video_script.txt] Output token name: [voice_settings] Title task: Generate realistic human voice-over Description of the task: Use the Create voice-over from text task to convert the video script into a realistic human voice-over using the voice settings determined in the previous task. Input(s) involved: [video_script.txt], [voice_settings] Output token name: [voice_over.mp3] Title task: Save voice-over to server Description of the task: Save the generated voice-over as an MP3 file using the Save content to server task. The MP3 file should have a maximum duration of 1 minute (60 seconds), mono channel, 16-bit depth, and a sampling rate of 44.1 kHz. Input(s) involved: [voice_over.mp3] Output token name: [voice_over_url] Title task: Output voice-over URL Description of the task: Output the URL of the saved MP3 file containing the voice-over, which can be used by other subagents in the video creation process. Input(s) involved: [voice_over_url] Output token name: [voice_over_url]

subagent3mermaid

<!DOCTYPE html> <html> <head> <title>Mermaid Diagram Example</title> <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script> </head> <body> <div class="mermaid"> graph TD A[Receive video script from Script Writer subagent] --> B{video_script.txt} B --> C[Analyze video script for voice-over settings] C --> D{voice_settings} B --> E[Generate realistic human voice-over] D --> E E --> F{voice_over.mp3} F --> G[Save voice-over to server] G --> H{voice_over_url} H --> I[Output voice-over URL] </div> <script> mermaid.initialize({ startOnLoad: true }); </script> </body> </html>

prompts-required

Based on the components listed above, we will need to write the following unique prompts for this agent workflow: Task: Generate Video Script Output of the task: A text file containing the transcription of what the newscaster will say about the given topic. Description: The prompt should instruct the LLM (e.g., GPT-4) to generate a video script based on the user's input about the video's subject matter. The prompt should specify the desired length of the script (approximately 1 minute of spoken content), the tone and style of the newscaster (e.g., professional, informative, engaging), and any other relevant details to ensure the generated script aligns with the user's expectations. Task: Generate Shotstack JSON Configuration Output of the task: A JSON file containing the timeline and layout instructions for combining the talking head video with the stock footage. Description: The prompt should instruct the LLM to generate a JSON file that specifies the timeline and layout for the final composite video. The prompt should include the following information: - The talking head video should be the base layer of the composition. - The 5 stock footage clips should be overlaid on top of the talking head video. - Each stock footage clip should appear for 5 seconds and be evenly distributed throughout the 1-minute video. - The prompt should also specify any other necessary parameters, such as the size and position of the stock footage clips, any transitions or effects, and any other relevant settings required by the Shotstack API. Task: Search for Stock Footage Output of the task: A collection of 5 royalty-free video clips relevant to the video's topic. Description: The prompt should instruct the LLM to generate search queries based on the video's topic to find relevant stock footage on platforms like Pexels. The prompt should specify the following criteria: - The clips should be royalty-free and suitable for use in the final video. - The clips should be relevant to the video's topic and complement the content of the video script. - The clips should be of high visual quality and resolution. - The prompt should also include any additional criteria, such as preferred video length, aspect ratio, or specific content to include or avoid. These three prompts cover the main tasks that require unique text generation from the LLM. The other components, such as the avatar image, voice-over audio, and talking head video, rely primarily on API calls and do not necessarily require custom prompts.